spreg.GM_ErrorRE¶

class spreg.GM_ErrorRE(y, x, w, slx_lags=0, slx_vars='all', time_effects=False, full_weights=False, regimes=None, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None, name_regimes=None, latex=False)[source]¶

GMM method for a spatial random effects panel model based on Kapoor, Kelejian and Prucha (2007) [KKP07].

Parameters:

yarray or pandas DataFrame: n*tx1 or nxt array for dependent variable
xarray or pandas DataFrame: Two dimensional array or DF with n*t rows and k columns for independent (exogenous) variable or n rows and k*t columns (note, must not include a constant term)
wspatial weights object: Spatial weights matrix, nxn
slx_lagsinteger: Number of spatial lags of X to include in the model specification. If slx_lags>0, the specification becomes of the SLX type.
slx_varseither “All” (default) or list of booleans to select x variables: to be lagged
time_effectsbool: If True, then include time dummies in the model specification (first omitted). Default: False
full_weights: boolean: Considers different weights for each of the 6 moment conditions if True or only 2 sets of weights for the first 3 and the last 3 moment conditions if False (default)
vmbool: If True, include variance-covariance matrix in summary results
name_ystr or list of strings: Name of dependent variable for use in output
name_xlist of strings: Names of independent variables for use in output
name_wstr: Name of weights matrix for use in output
name_dsstr: Name of dataset for use in output
latexbool: Specifies if summary is to be printed in latex format

Attributes:

outputdataframe: regression results pandas dataframe
summarystr: Summary of regression results and diagnostics (note: use in conjunction with the print command)
betasarray: kx1 array of estimated coefficients
uarray: nx1 array of residuals
predyarray: nx1 array of predicted y values
ninteger: Number of observations
tinteger: Number of time periods
kinteger: Number of variables for which coefficients are estimated (including the constant)
yarray: nx1 array for dependent variable
xarray: Two dimensional array with n rows and one column for each independent (exogenous) variable, including the constant
vmarray: Variance covariance matrix (kxk)
name_ystr: Name of dependent variable for use in output
name_xlist of strings: Names of independent variables for use in output
name_wstr: Name of weights matrix for use in output
name_dsstr: Name of dataset for use in output

Examples

>>> import libpysal
>>> import geopandas as gpd
>>> import numpy as np
>>> from spreg import GM_ErrorRE

In this example, we will use data on NCOVR US County Homicides (3085 areas) included in libpysal.

>>> libpysal.examples.load_example('NCOVR')
>>> db = gpd.read_file(libpysal.examples.get_path("NAT.shp"))
>>> name_ds = "NCOVR.shp"

Extract the HR (homicide rates) data in the 70’s, 80’s and 90’s from the dataframe and make it the dependent variable for the regression. Note that the data can also be passed in the long format instead of wide format (i.e. a vector with n*t rows and a single column for the dependent variable and a matrix of dimension n*txk for the independent variables).

>>> y_name = ['HR70','HR80','HR90']
>>> y = db[y_name]

Extract RD and PS in the same time periods from the dataframe to be used as independent variables in the regression. Data must be organized in a way that all time periods of a given variable are side-by-side and in the correct time order. By default a vector of ones will be added to the independent variables passed in.

>>> x_names = ['RD70','RD80','RD90','PS70','PS80','PS90']
>>> x = db[x_names]

Since we want to run a spatial error panel model, we need to specify the spatial weights matrix that includes the spatial configuration of the observations into the error component of the model. To do that, we can open an already existing gal file or create a new one. In this case, we will create one from NAT.shp.

>>> w = libpysal.weights.Queen.from_dataframe(db, use_index=True)
>>> w.transform = 'r'
>>> name_w = "NCOVR.gal"

We are all set with the preliminaries, we are good to run the model.

>>> reg = GM_ErrorRE(y, x, w)

Once we have run the model, we can explore a little bit the output with the command print(reg.summary).

__init__(y, x, w, slx_lags=0, slx_vars='all', time_effects=False, full_weights=False, regimes=None, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None, name_regimes=None, latex=False)[source]¶

Methods

__init__(y, x, w[, slx_lags, slx_vars, ...])

Attributes

`mean_y`
`std_y`

property mean_y¶

property std_y¶